class_docstr: Provides Lidstone-smoothed scores.
  
  In addition to initialization arguments from BaseNgramModel also requires
  a number by which to increase the counts, gamma.
docstr: Add-one smoothing: Lidstone or Laplace.
  
  To see what kind, look at `gamma` attribute on the class.
docstr_tok: ["Add-one","smoothing",":","Lidstone","or","Laplace",".","To","see","what","kind",",","look","at","`","gamma","`","attribute","on","the","class","."]
code: def unmasked_score(self, word, context=None):
      """Add-one smoothing: Lidstone or Laplace.
  
      To see what kind, look at `gamma` attribute on the class.
  
      """
      counts = self.context_counts(context)
      word_count = counts[word]
      norm_count = counts.N()
      return (word_count + self.gamma) / (
          norm_count + len(self.vocab) * self.gamma
      )
  
imports: from nltk.lm.api import LanguageModel, Smoothing
  from nltk.lm.smoothing import AbsoluteDiscounting, KneserNey, WittenBell
label_desc: Use image node_id 2 for calling the Lidstone obj's underlying member method code with example usage: obj.unmasked_score(word, context) and returns: unknown